Volume-outcome relationships for head and neck cancer surgery in a universal health care system
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Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: We aimed to assess whether surgeon and/or institution resection volume predicts long-term overall survival in head and neck cancer in a publicly funded healthcare system. STUDY DESIGN: Population-based retrospective cohort study. METHODS: Head and neck cancer patients in Ontario, Canada, who underwent a resection confirmed by both hospital-level and physician-level administrative data between 1993 and 2010, comprised our cohort (N = 5,720). Physician and hospital volumes were calculated based on number of cases performed in the year prior by the physician and at an institution performing each case, respectively. A multilevel hierarchical Cox regression model was used to estimate the effect on overall survival of each 25 increase in procedure volume. RESULTS: A crude model without patient or treatment characteristics demonstrated that both surgeon volume (hazard ratio [HR]: 0.927, 95% confidence interval [CI]: 0.879-0.978, P = .006) and hospital volume (HR: 0.980, 95% CI: 0.970-0.991, P = .0003) were associated with improved overall survival. After controlling for clustering and patient/treatment covariates, hospital volume (HR: 0.976, 95% CI: 0.955-0.997, P = .02), but not physician volume (HR: 1.042, 95% CI: 0.941-1.155, P = .43), remained a statistically significant predictor of overall survival. This translates into a 2.4% decrease in the HR for every 25 additional cases performed at an institution. CONCLUSIONS: Both high-volume surgeons and hospitals are predictors of better overall survival in head and neck cancer patients. However, the effect is largely explained by hospital volume. This benefit, at the institution level, could potentially be explained by important processes of care that contribute to overall survival.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it